The TAK1→IKKβ→TPL2→MKK1/MKK2 Signaling Cascade Regulates IL-33 Expression in Cystic Fibrosis Airway Epithelial Cells Following Infection by Pseudomonas aeruginosa
Bibliographic record
Abstract
In cystic fibrosis (CF), chronic respiratory infections result in an exaggerated and uncontrolled inflammatory response that ultimately lead to a decrease in pulmonary function. We have previously described the presence of the alarmin IL-33 in lung explants from CF patients. The signals regulating IL-33 expression in the airway epithelium following a gram-negative bacterial infection are currently unknown. Our objective was to characterize the pathways in CF airway epithelial cells (AECs) leading to an increase in IL-33 expression. We found that, in CF AECs expressing a deletion of a phenylalanine at position 508 of the gene coding for Cystic Fibrosis Transmembrane Conductance Regulator (CFTRdelF508), exposure to live Pseudomonas aeruginosa upregulates IL-33 via the TLR2 and TLR5 signaling pathways. This up-regulation can be partially or fully reverted by pre-incubating CFTRdelF508 AECs with a CFTR corrector (VX-809) and/or a CFTR potentiator (VX-770). Similarly, incubation with the CFTR corrector and/or the CFTR potentiator also decreased IL-8 expression in response to infection. Moreover, using different protein kinase inhibitors that target elements downstream of TLR signaling, we show that the TAK1→IKKβ→TPL2→MKK1/MKK2 pathway regulates IL-33 expression following an infection with P. aeruginosa. Our findings represent the first characterization of the signals regulating IL-33 expression in CF airway epithelial cells in response to a bacterial infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".